Breast cancer accounts for about 30% of all cancers
and 15% of cancer deaths in women. Advances in computer
assisted analysis hold promise for classifying subtypes of disease
and improving prognostic accuracy. We introduce a Grid-enabled
decision support system for performing automatic analysis of
imaged breast tissue microarrays. To date, we have processed
more than 100,000 digitized specimens (1200x1200 pixels
each) on IBM's World Community Grid (WCG). As part of
the Help Defeat Cancer (HDC) project, we have analyzed
the data returned from WCG along with retrospective patient
clinical profiles for a subset of 3744 breast tissue samples and
the results are reported in this paper. Texture based features
were extracted from the digitized images and isometric feature
mapping (ISOMAP) was applied to achieve nonlinear dimension
reduction. Iterative prototyping and testing were performed to
classify several major subtypes of breast cancer. Overall the
most reliable approach was gentle AdaBoost using an eight node
classification and regression tree (CART) as the weak learner.
Using the proposed algorithm, a binary classification accuracy
of 89% and the multi-class accuracy of 80% were achieved.
Throughout the course of the experiments only 30% of the
dataset was used for training.